




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (PNDMScheduler,)
						UpperCamelCase      =       (("""num_inference_steps""", 50),)

						def      A__    (		self :str     ,			**__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						int										={
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**__snake_case			)
										return config

						def      A__    (		self :List[Any]     ,			__snake_case :Union[str, Any]=0     ,			**__snake_case :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Any										=dict(self.forward_default_kwargs			)
										__magic_name__						:						str										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						int										=self.dummy_sample
										__magic_name__						:						Dict										=0.1 * sample
										__magic_name__						:						Tuple										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Optional[int]										=self.get_scheduler_config(**__snake_case			)
														__magic_name__						:						Dict										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)
														# copy over dummy past residuals
														__magic_name__						:						Tuple										=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						int										=scheduler_class.from_pretrained(__snake_case			)
																		new_scheduler.set_timesteps(__snake_case			)
																		# copy over dummy past residuals
																		__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														__magic_name__						:						int										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=new_scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

														assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

														__magic_name__						:						Tuple										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						str										=new_scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

														assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]=0     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=dict(self.forward_default_kwargs			)
										__magic_name__						:						Optional[int]										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						List[Any]										=self.dummy_sample
										__magic_name__						:						Optional[Any]										=0.1 * sample
										__magic_name__						:						Optional[int]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						List[str]										=self.get_scheduler_config()
														__magic_name__						:						int										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						Optional[int]										=scheduler_class.from_pretrained(__snake_case			)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(__snake_case			)

																		# copy over dummy past residual (must be after setting timesteps)
																		__magic_name__						:						Any										=dummy_past_residuals[:]

														__magic_name__						:						List[str]										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=new_scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

														assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

														__magic_name__						:						Any										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						List[Any]										=new_scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

														assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"

						def      A__    (		self :Any     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.scheduler_classes[0]
										__magic_name__						:						Dict										=self.get_scheduler_config(**__snake_case			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)

										__magic_name__						:						int										=10
										__magic_name__						:						Tuple										=self.dummy_model()
										__magic_name__						:						Dict										=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case			)

										for i, t in enumerate(scheduler.prk_timesteps			):
														__magic_name__						:						Optional[int]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						List[str]										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps			):
														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Union[str, Any]										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										return sample

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=dict(self.forward_default_kwargs			)

										__magic_name__						:						Tuple										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						str										=self.get_scheduler_config()
														__magic_name__						:						Dict										=scheduler_class(**__snake_case			)

														__magic_name__						:						Optional[int]										=self.dummy_sample
														__magic_name__						:						int										=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case     ,			"""set_timesteps"""			):
																		scheduler.set_timesteps(__snake_case			)
														elif num_inference_steps is not None and not hasattr(__snake_case     ,			"""set_timesteps"""			):
																		__magic_name__						:						List[str]										=num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														__magic_name__						:						List[str]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														__magic_name__						:						int										=dummy_past_residuals[:]

														__magic_name__						:						Union[str, Any]										=scheduler.step_prk(__snake_case     ,			0     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						int										=scheduler.step_prk(__snake_case     ,			1     ,			__snake_case     ,			**__snake_case			).prev_sample

														self.assertEqual(output_a.shape     ,			sample.shape			)
														self.assertEqual(output_a.shape     ,			output_a.shape			)

														__magic_name__						:						List[Any]										=scheduler.step_plms(__snake_case     ,			0     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						str										=scheduler.step_plms(__snake_case     ,			1     ,			__snake_case     ,			**__snake_case			).prev_sample

														self.assertEqual(output_a.shape     ,			sample.shape			)
														self.assertEqual(output_a.shape     ,			output_a.shape			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case			)

						def      A__    (		self :int			):
										'''simple docstring'''

										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__snake_case			)

										__magic_name__						:						str										=self.scheduler_classes[0]
										__magic_name__						:						Tuple										=self.get_scheduler_config(steps_offset=1			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)
										scheduler.set_timesteps(10			)
										assert torch.equal(
										    scheduler.timesteps     ,			torch.LongTensor(
										        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]			)     ,			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.0001, 0.001]     ,			[0.002, 0.02]			):
														self.check_over_configs(beta_start=__snake_case     ,			beta_end=__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=__snake_case			)

						def      A__    (		self :str			):
										'''simple docstring'''

										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for t in [1, 5, 10]:
														self.check_over_forward(time_step=__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]     ,			[10, 50, 1_00]			):
														self.check_over_forward(num_inference_steps=__snake_case			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=27

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Union[str, Any]										=self.dummy_sample
														__magic_name__						:						str										=0.1 * sample

														__magic_name__						:						List[str]										=self.get_scheduler_config()
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

														scheduler.set_timesteps(__snake_case			)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]			):
																		__magic_name__						:						Dict										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										with self.assertRaises(__snake_case			):
														__magic_name__						:						List[Any]										=self.scheduler_classes[0]
														__magic_name__						:						Optional[Any]										=self.get_scheduler_config()
														__magic_name__						:						List[str]										=scheduler_class(**__snake_case			)

														scheduler.step_plms(self.dummy_sample     ,			1     ,			self.dummy_sample			).prev_sample

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.full_loop()
										__magic_name__						:						int										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Optional[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 198.1318			) < 1E-2
										assert abs(result_mean.item() - 0.2580			) < 1E-3

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.full_loop(prediction_type="""v_prediction"""			)
										__magic_name__						:						List[str]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[str]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 67.3986			) < 1E-2
										assert abs(result_mean.item() - 0.0878			) < 1E-3

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.full_loop(set_alpha_to_one=__snake_case     ,			beta_start=0.01			)
										__magic_name__						:						Tuple										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 230.0399			) < 1E-2
										assert abs(result_mean.item() - 0.2995			) < 1E-3


						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.full_loop(set_alpha_to_one=__snake_case     ,			beta_start=0.01			)
										__magic_name__						:						Union[str, Any]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Any										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 186.9482			) < 1E-2
										assert abs(result_mean.item() - 0.2434			) < 1E-3






"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor



class 	__a	(   unittest.TestCase						):






       def __init__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Optional[Any]=7							,   UpperCAmelCase_			:				List[Any]=3							,   UpperCAmelCase_			:				Optional[int]=30							,   UpperCAmelCase_			:				Any=400							,   UpperCAmelCase_			:				Any=True							,   UpperCAmelCase_			:				Dict=None							,   UpperCAmelCase_			:				Dict=0.9							,   UpperCAmelCase_			:				Tuple=None							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				Optional[int]=[0.5, 0.5, 0.5]							,   UpperCAmelCase_			:				Union[str, Any]=[0.5, 0.5, 0.5]							,   )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		size if size is not None else {"shortest_edge": 30}
              UpperCamelCase       			=		crop_size if crop_size is not None else {"height": 30, "width": 30}
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		min_resolution
              UpperCamelCase       			=		max_resolution
              UpperCamelCase       			=		do_resize_and_center_crop
              UpperCamelCase       			=		size
              UpperCamelCase       			=		crop_pct
              UpperCamelCase       			=		crop_size
              UpperCamelCase       			=		do_normalize
              UpperCamelCase       			=		image_mean
              UpperCamelCase       			=		image_std






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              return {
                  "size": self.size,
                  "do_resize_and_center_crop": self.do_resize_and_center_crop,
                  "crop_pct": self.crop_pct,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }





@require_torch
@require_vision
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       List[Any]				=      PoolFormerImageProcessor if is_vision_available() else None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		PoolFormerImageProcessingTester(self      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              return self.image_processor_tester.prepare_image_processor_dict()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_resize_and_center_crop"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "size"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "crop_pct"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_normalize"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_mean"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_std"      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 30}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 30, "width": 30}      )

              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict							,   size=42							,   crop_size=84      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 42}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 84, "width": 84}      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   Image.Image      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values

              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[str]:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   numpify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   np.ndarray      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	str:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   torchify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   torch.Tensor      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )





